'''simple docstring'''




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=5							)      ->       Dict:
    # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
    assert masked_input.count('''<mask>'''							) == 1
    __lowerCamelCase            =    torch.tensor(tokenizer.encode(UpperCamelCase__							,    add_special_tokens=UpperCamelCase__							)							).unsqueeze(0							)  # Batch size 1
    __lowerCamelCase            =    model(UpperCamelCase__							)[0]  # The last hidden-state is the first element of the output tuple
    __lowerCamelCase            =    (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
    __lowerCamelCase            =    logits[0, masked_index, :]
    __lowerCamelCase            =    logits.softmax(dim=0							)
    __lowerCamelCase      ,    __lowerCamelCase            =    prob.topk(k=UpperCamelCase__							,    dim=0							)
    __lowerCamelCase            =    ''' '''.join(
        [tokenizer.convert_ids_to_tokens(indices[i].item()							) for i in range(len(UpperCamelCase__							)							)]							)
    __lowerCamelCase            =    tokenizer.mask_token
    __lowerCamelCase            =    []
    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''							)							):
        __lowerCamelCase            =    predicted_token_bpe.replace('''\u2581'''							,    ''' '''							)
        if " {0}".format(UpperCamelCase__							) in masked_input:
            topk_filled_outputs.append(
                (
                    masked_input.replace(''' {0}'''.format(UpperCamelCase__							)							,    UpperCamelCase__							),
                    values[index].item(),
                    predicted_token,
                )							)
        else:
            topk_filled_outputs.append(
                (
                    masked_input.replace(UpperCamelCase__							,    UpperCamelCase__							),
                    values[index].item(),
                    predicted_token,
                )							)
    return topk_filled_outputs


__UpperCAmelCase					=CamembertTokenizer.from_pretrained("camembert-base")
__UpperCAmelCase					=CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

__UpperCAmelCase					="Le camembert est <mask> :)"



print(fill_mask(masked_input, model, tokenizer, topk=3))

'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class        a__ :






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         return self.get_dummy_input()






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         if self.block_type == "down":
             return (4, 32, 16, 16)
         elif self.block_type == "mid":
             return (4, 32, 32, 32)
         elif self.block_type == "up":
             return (4, 32, 64, 64)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[Any]=True    ,							a						:						Dict=False    ,							a						:						Optional[Any]=False    ,							a						:						List[Any]=False    ,							):





         """simple docstring"""



         __lowerCamelCase            =    4
         __lowerCamelCase            =    32
         __lowerCamelCase            =    (32, 32)

         __lowerCamelCase            =    torch.manual_seed(0       )
         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    (batch_size, num_channels) + sizes
         __lowerCamelCase            =    randn_tensor(a    ,							generator=a    ,							device=a       )
         __lowerCamelCase            =    {'''hidden_states''': hidden_states}

         if include_temb:
             __lowerCamelCase            =    1_28
             __lowerCamelCase            =    randn_tensor((batch_size, temb_channels)    ,							generator=a    ,							device=a       )

         if include_res_hidden_states_tuple:
             __lowerCamelCase            =    torch.manual_seed(1       )
             __lowerCamelCase            =    (randn_tensor(a    ,							generator=a    ,							device=a       ),)

         if include_encoder_hidden_states:
             __lowerCamelCase            =    floats_tensor((batch_size, 32, 32)       ).to(a       )

         if include_skip_sample:
             __lowerCamelCase            =    randn_tensor(((batch_size, 3) + sizes)    ,							generator=a    ,							device=a       )

         return dummy_input






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    {
             '''in_channels''': 32,
             '''out_channels''': 32,
             '''temb_channels''': 1_28,
         }
         if self.block_type == "up":
             __lowerCamelCase            =    32

         if self.block_type == "mid":
             init_dict.pop('''out_channels'''       )

         __lowerCamelCase            =    self.dummy_input
         return init_dict, inputs_dict






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         unet_block.to(a       )
         unet_block.eval()

         with torch.no_grad():
             __lowerCamelCase            =    unet_block(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         self.assertEqual(output.shape    ,							self.output_shape       )

         __lowerCamelCase            =    output[0, -1, -3:, -3:]
         __lowerCamelCase            =    torch.tensor(a       ).to(a       )
         assert torch_all_close(output_slice.flatten()    ,							a    ,							atol=5e-3       )






     @unittest.skipIf(torch_device == '''mps'''    ,							'''Training is not supported in mps'''       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         model.to(a       )
         model.train()
         __lowerCamelCase            =    model(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    randn_tensor(output.shape    ,							device=a       )
         __lowerCamelCase            =    torch.nn.functional.mse_loss(a    ,							a       )
         loss.backward()
